SOTAVerified

Reinforcement Learning (RL)

Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 5651–5675 of 15113 papers

TitleStatusHype
Safety aware model-based reinforcement learning for optimal control of a class of output-feedback nonlinear systems—0
Safety-Aware Multi-Agent Apprenticeship Learning—0
Safe Autonomous Racing via Approximate Reachability on Ego-vision—0
Safety-Aware Reinforcement Learning for Electric Vehicle Charging Station Management in Distribution Network—0
Safety-Aware Reinforcement Learning for Control via Risk-Sensitive Action-Value Iteration and Quantile Regression—0
Safety Aware Reinforcement Learning (SARL)—0
Safety-Aware Task Composition for Discrete and Continuous Reinforcement Learning—0
Safety Correction from Baseline: Towards the Risk-aware Policy in Robotics via Dual-agent Reinforcement Learning—0
Safety-Enhanced Self-Learning for Optimal Power Converter Control—0
Safety Enhancement for Deep Reinforcement Learning in Autonomous Separation Assurance—0
Safety Filtering for Reinforcement Learning-based Adaptive Cruise Control—0
Safety-guaranteed Reinforcement Learning based on Multi-class Support Vector Machine—0
Safety-Guided Deep Reinforcement Learning via Online Gaussian Process Estimation—0
Safety-Oriented Pruning and Interpretation of Reinforcement Learning Policies—0
Safety through Permissibility: Shield Construction for Fast and Safe Reinforcement Learning—0
Black-Box Safety Validation of Autonomous Systems: A Multi-Fidelity Reinforcement Learning Approach—0
Safety Verification of Model Based Reinforcement Learning Controllers—0
SaFormer: A Conditional Sequence Modeling Approach to Offline Safe Reinforcement Learning—0
SA-IGA: A Multiagent Reinforcement Learning Method Towards Socially Optimal Outcomes—0
SAINT-ACC: Safety-Aware Intelligent Adaptive Cruise Control for Autonomous Vehicles Using Deep Reinforcement Learning—0
Saliency-based Sequential Image Attention with Multiset Prediction—0
SaLinA: Sequential Learning of Agents—0
SAMG: State-Action-Aware Offline-to-Online Reinforcement Learning with Offline Model Guidance—0
Sample and Oracle Efficient Reinforcement Learning for MDPs with Linearly-Realizable Value Functions—0
Sample-based Distributional Policy Gradient—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PPGMean Normalized Performance0.76—Unverified
2PPOMean Normalized Performance0.58—Unverified